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Land subsidence occurs in Ha Long and Cam Pha cities due to human activities in which the underground coal mining exploitation plays a crucial responsibility. For mapping the spatial distribution o...

The seas of Southeast Asia are home to some of the world’s most diverse ecosystems and resources that support the livelihoods and wellbeing of millions of people. Climate change will bring temperature changes, acidification and other environmental change, with uncertain consequences for human and natural systems in the region. We present the first regional-scale projections of change in the marine environment up to the end of 21st century. A coupled physical-biogeochemical model with a resolution of 0.1° (approximately 11 km) was used to create projections of future environmental conditions under two greenhouse gas scenarios, RCP4.5 and RCP8.5. These show a sea that is warming by 1.1–2.9°C through the 21st century, with surface pH falling by up to 0.02 and dissolved oxygen decreasing by 5 to 13 mmol m− 3. Changes for different parts of the region, including four sensitive coastal sites, are presented. The changes reach all parts of the water column and many places are projected to experience conditions well outside the range seen at the start of the century. Altered species distribution and damage to coral reefs resulting from this environmental change would have consequences for biodiversity, for the livelihoods of small-scale fishers and for the food security of coastal communities across the region. Projections of this type are a key tool for communities planning how they will adapt to the challenge of climate change.

Identifying suitable parameter sets for use in catchment modelling remains a critical issue in hydrology. This paper describes an early stopping technique (EST) for use during calibration of a multi-parameter urban catchment modelling system. The proposed method takes advantage of MODE and lower confidence limit (LCL) functions in statistical analysis of spanning set of objective function values. The paper also introduces a monitoring process and regularization techniques to avoid under/overfitting during the calibration and to enhance generalisation performance. The methodology is assessed using SWMM and linked with a Genetic Algorithm for calibration of a Powells Creek catchment model in Sydney, Australia. Results demonstrate that the statistical spanning set analysis approach overcomes issues of poor interpretation and deterioration in the model’s generalisation properties. By stopping early, the calibration process avoided overfitting; this was indicated by too closely fitting to the calibration dataset and a failure to fit to the monitoring dataset.

In the last few decades tremendous progress has been made in the use of catchment models for the analysis and understanding of hydrologic systems. A common application involves the use of these models to predict flows at catchment outputs. However, the outputs predicted by these models are often deterministic because they focused only on the most probable forecast without an explicit estimate of the associated uncertainty. This paper uses Bayesian and Generalized Likelihood Uncertainty Estimation (GLUE) approaches to estimate uncertainty in catchment modelling parameter values and uncertainty in design flow estimates. Testing of join probability of both these estimates has been conducted for a monsoon catchment in Vietnam. The paper focuses on computational efficiency and the differences in results, regardless of the philosophies and mathematical rigor of both methods. It was found that the application of GLUE and Bayesian techniques resulted in parameter values that were statistically different. The design flood quantiles estimated by the GLUE method were less scattered than those resulting from the Bayesian approach when using a closer threshold value (1 standard deviation departed from the mean). More studies are required to evaluate the impact of threshold in GLUE on design flood estimation.